A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 8 Issue 4
Apr.  2021

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
Xiuyu Zhang, Ruijing Jing, Zhiwei Li, Zhi Li, Xinkai Chen, and Chun-Yi Su, "Adaptive Pseudo Inverse Control for a Class of Nonlinear Asymmetric and Saturated Nonlinear Hysteretic Systems," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 916-928, Apr. 2021. doi: 10.1109/JAS.2020.1003435
Citation: Xiuyu Zhang, Ruijing Jing, Zhiwei Li, Zhi Li, Xinkai Chen, and Chun-Yi Su, "Adaptive Pseudo Inverse Control for a Class of Nonlinear Asymmetric and Saturated Nonlinear Hysteretic Systems," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 916-928, Apr. 2021. doi: 10.1109/JAS.2020.1003435

Adaptive Pseudo Inverse Control for a Class of Nonlinear Asymmetric and Saturated Nonlinear Hysteretic Systems

doi: 10.1109/JAS.2020.1003435
Funds:  This work was supported in part by the National Natural Science Foundation of China (61673101, 61973131, 61733006, U1813201), the Japan Society for the Promotion of Science (C-18K04212), the Science and Technology Project of Jilin Province (20180201009SF, 20170414011GH, 20180201004SF, 20180101069JC), the Fundamental Research Funds for the Central Universities (N2008002), and “Xing Liao Ying Cai” Program (XLYC1907073)
More Information
  • This paper aims at eliminating the asymmetric and saturated hysteresis nonlinearities by designing hysteresis pseudo inverse compensator and robust adaptive dynamic surface control (DSC) scheme. The “pseudo inverse” means that an on-line calculation mechanism of approximate control signal is developed by applying a searching method to the designed temporary control signal where the true control signal is included. The main contributions are summarized as: 1) to our best knowledge, it is the first time to compensate the asymmetric and saturated hysteresis by using hysteresis pseudo inverse compensator because the construction of the true saturated-type hysteresis inverse model is very difficult; 2) by designing the saturated-type hysteresis pseudo inverse compensator, the construction of true explicit hysteresis inverse and the identifications of its corresponding unknown parameters are not required when dealing with the saturated-type hysteresis; 3) by combining DSC technique with the tracking error transformed function, the “explosion of complexity” problem in backstepping method is overcome and the prespecified tracking performance is achieved. Analysis of stability and experimental results on the hardware-in-loop platform illustrate the effectiveness of the proposed adaptive pseudo inverse control scheme.

     

  • loading
  • Manuscript received August 10, 2020; accepted September 7, 2020. This work was supported in part by the National Natural Science Foundation of China (61673101, 61973131, 61733006, U1813201), the Japan Society for the Promotion of Science (C18K04212), the Science and Technology Project of Jilin Province (20180201009SF, 20170414011GH, 20180201004SF, 20180101069JC), the Fundamental Research Funds for the Central Univer-sities (N2008002), and “Xing Liao Ying Cai” Program (XLYC1907073). Recommended by Associate Editor Yebin Wang. (Corresponding author: Zhiwei Li.) Citation: X. Y. Zhang, R. J. Jing, Z. W. Li, Z. Li, X. K. Chen, and C.-Y. Su, “Adaptive pseudo inverse control for a class of nonlinear asymmetric and saturated nonlinear hysteretic systems,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 916–928, Apr. 2021. X. Zhang, R. Jing, and Z. W. Li are with the School of Automation Engin- eering, Northeast Electric Power University, and also with Jilin Province International Research Center of Precision Drive and Intelligent Control, Jilin 132012, China (e-mail: zhangxiuyu80@163.com; 870565824@qq.com; 657580390@qq.com). Z. Li is with the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China (e-mail: gavinlizhi@gmail.com).
    X. Chen is with the Department of Electronic and Information Systems, Shibaura Institute of Technology,Saitama 337-8570,Japan (e-mail: chen@shibaura-it.ac.jp). C.-Y. Su is with the Department of Mechanical and Industrial Engineering, Concordia University, QC, Montreal H3B 1R6, Canada (e-mail: cysu@alcor.concordia.ca). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.iece.org. Digital Object Identifier 10.1109/JAS.2020.1003435
  • [1]
    S. Mittal and C. H. Menq, “Hysteresis compensation in electromagnetic actuators through Preisach model inversion,” IEEE/ASME Trans. Mech., vol. 5, no. 4, pp. 394–409, Dec. 2000. doi: 10.1109/3516.891051
    [2]
    X. B. Tan and J. S. Baras, “Modeling and control of hysteresis in magnetostrictive actuators,” Automatica, vol. 40, no. 9, pp. 1469–1480, Sep. 2004. doi: 10.1016/j.automatica.2004.04.006
    [3]
    G. V. Webb, D. C. Lagoudas, and A. J. Kurdila, “Hysteresis modeling of SMA actuators for control applications,” J. Intell. Mater. Syst. Struct., vol. 9, no. 6, pp. 432–448, Jun. 1998. doi: 10.1177/1045389X9800900605
    [4]
    Y. Stepanenko and C. Y. Su, “Intelligent control of piezoelectric actuators,” in Proc. IEEE Conf. Decision and Control, Tampa, USA, 1998, pp. 4234−4239.
    [5]
    G. Tao and P. V. Kokotovic, “Adaptive control of plants with unknown hystereses,” IEEE Trans. Autom. Control, vol. 40, no. 2, pp. 200–212, Feb. 1995. doi: 10.1109/9.341778
    [6]
    S. O. R. Moheimani and G. C. Goodwin, “Guest editorial Introduction to the special issue on dynamics and control of smart structures,” IEEE Trans. Control Syst. Technol., vol. 9, no. 1, pp. 3–4, Jan. 2001. doi: 10.1109/TCST.2001.896740
    [7]
    G. Tao and F. L. Lewis, Adaptive Control of Nonsmooth Dynamic Systems. London: Springer, 2001.
    [8]
    X. B. Tan and J. S. Baras, “Adaptive identification and control of hysteresis in smart materials,” IEEE Trans. Autom. Control, vol. 50, no. 6, pp. 827–839, Jun. 2005. doi: 10.1109/TAC.2005.849215
    [9]
    R. V. Iyer, X. B. Tan, and P. S. Krishnaprasad, “Approximate inversion of the Preisach hysteresis operator with application to control of smart actuators,” IEEE Trans. Autom. Control, vol. 50, no. 6, pp. 798–810, Jun. 2005. doi: 10.1109/TAC.2005.849205
    [10]
    W. S. Galinaitis, “Two methods for modeling scalar hysteresis and their use in controlling actuators with hysteresis,” Ph.D. dissertation, Virginia Polytechnic Institute and State Univ., Blacksburg, USA, 1999.
    [11]
    P. Krejci and K. Kuhnen, “Inverse control of systems with hysteresis and creep,” IEE Proc.-Control Theory Appl., vol. 148, no. 3, pp. 185–192, Jun. 2001. doi: 10.1049/ip-cta:20010375
    [12]
    Z. Li, J. J. Shan, and U. Gabbert, “Inverse compensation of hysteresis using Krasnoselskii-Pokrovskii model,” IEEE/ASME Trans. Mech., vol. 23, no. 2, pp. 966–971, Feb. 2018. doi: 10.1109/TMECH.2018.2805761
    [13]
    X. Y. Zhang, Y. Wang, C. L. Wang, C. Y. Su, Z. Li, and X. K. Chen, “Adaptive estimated inverse output-feedback quantized control for piezoelectric positioning stage,” IEEE Trans. Cybern., vol. 49, no. 6, pp. 2106–2118, Apr. 2019. doi: 10.1109/TCYB.2018.2826519
    [14]
    M. L. Zhou, Y. N. Zhang, K. Ji, and D. Zhu, “Model reference adaptive control based on KP model for magnetically controlled shape memory alloy actuators,” J. Appl. Biomater. Funct. Mater., vol. 15, no. 1 Suppl, pp. 31–37, Jun. 2017.
    [15]
    K. Kuhnen and H. Janocha, “Adaptive inverse control of piezoelectric actuators with hysteresis operators,” in Proc. European Control Conf., Karlsruhe, Germany, 1999, pp. 791−796.
    [16]
    H. Q. Wang, H. K. Shen, X. J. Xie, T. Hayat, and F. E. Alsaadi, “Robust adaptive neural control for pure-feedback stochastic nonlinear systems with Prandtl-Ishlinskii hysteresis,” Neurocomputing, vol. 314, pp. 169–176, Nov. 2018. doi: 10.1016/j.neucom.2018.04.023
    [17]
    S. R. Li, Z. L. Shao, P. D. Xu, and H. Q. Yin, “Robust adaptive control for coordinated constrained multiple flexible joint manipulators with hysteresis loop,” Math. Probl. Eng., vol. 2018, Article No. 2507637, May 2018.
    [18]
    X. Y. Zhang, Z. Li, C. Y. Su, and Y. Lin, “Robust adaptive output-feedback control for a class of nonlinear systems with hysteresis compensation controller,” Int. J. Adapt. Control Signal Process., vol. 31, no. 11, pp. 1636–1654, Nov. 2017. doi: 10.1002/acs.2790
    [19]
    Y. J. Liu, L. Ma, L. Liu, S. C. Tong, and C. L. P. Chen, “Adaptive neural network learning controller design for a class of nonlinear systems with time-varying state constraints,” IEEE Trans. Neural Netw. Learn. Syst., vol. 31, no. 1, pp. 66–75, Jan. 2020. doi: 10.1109/TNNLS.2019.2899589
    [20]
    X. Y. Zhang, J. Wang, and C. P. Liu, “Robust adaptive dynamic surface control for metal cutting system with hysteresis input,” in Proc. 11th World Congr. Intelligent Control and Automation, Shenyang, China, 2014, pp. 1534−1539.
    [21]
    X. K. Chen, T. Hisayama, and C. Y. Su, “Adaptive control for uncertain continuous-time systems using implicit inversion of Prandtl-Ishlinskii hysteresis representation,” IEEE Trans. Autom. Control, vol. 55, no. 10, pp. 2357–2363, Oct. 2010. doi: 10.1109/TAC.2010.2053737
    [22]
    X. K. Chen, C. Y. Su, Z. Li, and F. Yang, “Design of implementable adaptive control for micro/Nano positioning system driven by piezoelectric actuator,” IEEE Trans. Ind. Electron., vol. 63, no. 10, pp. 6471–6481, Oct. 2016. doi: 10.1109/TIE.2016.2573270
    [23]
    C. H. Wang, “Adaptive dynamic surface control of parametric uncertain and disturbed strict-feedback nonlinear systems,” Adv. Differ. Equ., vol. 2019, no. 1, Article No. 29, Jan. 2019. doi: 10.1186/s13662-019-1971-1
    [24]
    J. L. Sun and C. S. Liu, “Adaptive dynamic surface-based differential games for a class of pure-feedback nonlinear systems with output constraints,” Int. J. Control, vol. 93, no. 6, pp. 1291–1302, 2020. doi: 10.1080/00207179.2018.1503724
    [25]
    M. Won and J. K. Hedrick, “Multiple-surface sliding control of a class of uncertain nonlinear systemsm,” Int. J. Control, vol. 64, no. 4, pp. 693–706, Apr. 1996. doi: 10.1080/00207179608921650
    [26]
    D. Swaroop, J. K. Hedrick, P. P. Yip, and J. C. Gerdes, “Dynamic Surface Control for a Class of Nonlinear Systems,” IEEE Trans. Autom. Control, vol. 45, no. 10, pp. 1893–1899, Oct. 2000. doi: 10.1109/TAC.2000.880994
    [27]
    D. Swaroop, J. C. Gerdes, P. P. Yip, and J. K. Hedrick, “Dynamic surface control of nonlinear systems,” in Proc. American Control Conf., Albuquerque, USA, 1997, pp. 3028−3034.
    [28]
    B. Song, A. Howell, and K. Hedrick, “Dynamic surface control design for a class of nonlinear systems,” in Proc. 40th IEEE Conf. Decision and Control, Orlando, USA, 2001, pp. 2797−2802.
    [29]
    S. C. Tong, K. K. Sun, and S. Sui, “Observer-based adaptive fuzzy decentralized optimal control design for strict-feedback nonlinear large-scale systems,” IEEE Trans. Fuzzy Syst., vol. 26, no. 2, pp. 569–584, Feb. 2018. doi: 10.1109/TFUZZ.2017.2686373
    [30]
    S. Sui, S. C. Tong, and C. L. P. Chen, “Finite-time filter decentralized control for nonstrict-feedback nonlinear large-scale systems,” IEEE Trans. Fuzzy Syst., vol. 26, no. 6, pp. 3289–3300, Jun. 2018. doi: 10.1109/TFUZZ.2018.2821629
    [31]
    S. Sui, C. L. P. Chen, and S. C. Tong, “Fuzzy adaptive finite-time control design for nontriangular stochastic nonlinear systems,” IEEE Trans. Fuzzy Syst., vol. 27, no. 1, pp. 172–184, Jan. 2019. doi: 10.1109/TFUZZ.2018.2882167
    [32]
    S. Sui, C. L. P. Chen, and S. C. Tong, “Neural network filtering control design for nontriangular structure switched nonlinear systems in finite time,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 7, pp. 2153–2162, Jul. 2019. doi: 10.1109/TNNLS.2018.2876352
    [33]
    S. C. Tong and Y. M. Li, “Robust adaptive fuzzy backstepping output feedback tracking control for nonlinear system with dynamic uncertainties,” Sci. China Inf. Sci., vol. 53, no. 2, pp. 307–324, Feb. 2010. doi: 10.1007/s11432-010-0031-y
    [34]
    S. C. Tong and Y. M. Li, “Observer-based adaptive fuzzy backstepping control of uncertain nonlinear pure-feedback systems,” Sci. China Inf. Sci., vol. 57, no. 1, Article No. 012204, Jan. 2014.
    [35]
    X. Y. Zhang, Y. Lin, and J. Q. Mao, “A robust adaptive dynamic surface control for a class of nonlinear systems with unknown Prandtl- Ishilinskii hysteresis,” Int. J. Robust Nonlinear Control, vol. 21, no. 13, pp. 1541–1561, Sep. 2011. doi: 10.1002/rnc.1652
    [36]
    Y. J. Liu, Q. Zeng, S. C. Tong, C. L. P. Chen, and L. Liu, “Actuator failure compensation-based adaptive control of active suspension systems with prescribed performance,” IEEE Trans. Ind. Electron., vol. 67, no. 8, pp. 7044–7053, Aug. 2020. doi: 10.1109/TIE.2019.2937037
    [37]
    X. Y. Zhang, C. Y. Su, Z. Li, X. M. Li, C. H. Xiong, and Y. Lin, “Fuzzy approximator based adaptive dynamic surface control for unknown time delay nonlinear systems with input asymmetric hysteresis nonlinearities,” IEEE Trans. Syst.,Man,Cybern.:Syst., vol. 47, no. 8, pp. 2218–2232, Aug. 2017. doi: 10.1109/TSMC.2016.2641926
    [38]
    C. P. Bechlioulis and G. A. Rovithakis, “Adaptive control with guaranteed transient and steady state tracking error bounds for strict feedback systems,” Automatica, vol. 45, no. 2, pp. 532–538, Feb. 2009. doi: 10.1016/j.automatica.2008.08.012
    [39]
    X. Y. Zhang, X. K. Chen, G. Q. Zhu, and C. Y. Su, “Output feedback adaptive motion control and its experimental verification for time- delay nonlinear systems with asymmetric hysteresis,” IEEE Trans. Ind. Electron., vol. 67, no. 8, pp. 6824–6834, Aug. 2020. doi: 10.1109/TIE.2019.2938460
    [40]
    S. S. Ge and J. Wang, “Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems,” IEEE Trans. Neural Netw., vol. 13, no. 6, pp. 1409–1419, Jun. 2002. doi: 10.1109/TNN.2002.804306
    [41]
    M. Wang, B. Chen, X. P. Liu, and P. Shi, “Adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear time-delay systems,” Fuzzy Sets Syst., vol. 159, no. 8, pp. 949–967, Aug. 2008. doi: 10.1016/j.fss.2007.12.022
    [42]
    M. Wang, B. Chen, and S. L. Dai, “Direct adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear systems,” Fuzzy Sets Syst., vol. 158, no. 24, pp. 2655–2670, Dec. 2007. doi: 10.1016/j.fss.2007.06.001
    [43]
    Y. S. Yang and C. J. Zhou, “Robust adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear systems via small-gain approach,” Inf. Sci., vol. 170, no. 2−4, pp. 211–234, Feb. 2005.
    [44]
    Z. T. Ding, “Adaptive control of non-linear systems with unknown virtual control coefficients,” Int. J. Adapt. Control Signal Process., vol. 14, no. 5, pp. 505–517, Aug. 2000. doi: 10.1002/1099-1115(200008)14:5<505::AID-ACS610>3.0.CO;2-N
    [45]
    B. B. Ren, P. P. San, S. S. Ge, and T. H. Lee, “Adaptive dynamic surface control for a class of strict-feedback nonlinear systems with unknown backlash-like hysteresis,” in Proc. American Control Conf., St. Louis, USA, 2009, pp. 4482−4487.
    [46]
    B. B. Ren, S. S. Ge, C. Y. Su, and T. H. Lee, “Adaptive neural control for a class of uncertain nonlinear systems in pure-feedback form with hysteresis input,” IEEE Trans. Syst.,Man,Cybern.,Part B, vol. 39, no. 2, pp. 431–443, Feb. 2009. doi: 10.1109/TSMCB.2008.2006368
    [47]
    Q. Q. Wang and C. Y. Su, “Robust adaptive control of a class of nonlinear systems including actuator hysteresis with Prandtl–Ishlinskii presentations,” Automatica, vol. 42, no. 5, pp. 859–867, May 2006. doi: 10.1016/j.automatica.2006.01.018
    [48]
    C. Y. Su, Q. Q. Wang, X. K. Chen, and S. Rakheja, “Adaptive variable structure control of a class of nonlinear systems with unknown Prandtl- Ishlinskii hysteresis,” IEEE Trans. Autom. Control, vol. 50, no. 12, pp. 2069–2074, Dec. 2005. doi: 10.1109/TAC.2005.860260
    [49]
    M. A. Krasnoselskii and A. V. Pokrovskii, Systems with Hysteresis. Berlin, Heidelberg, Germany: Springer Science & Business Media, 2012.
    [50]
    B. Yao and M. Tomizuka, “Adaptive robust control of SISO nonlinear systems in a semi-strict feedback form,” Automatica, vol. 33, no. 5, pp. 893–900, May 1997. doi: 10.1016/S0005-1098(96)00222-1

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(17)

    Article Metrics

    Article views (949) PDF downloads(54) Cited by()

    Highlights

    • A hysteresis pseudo inverse compensator is developed for the high-precision control of continuous-time nonlinear systems with asymmetric and saturated hysteresis.
    • Combined with the hysteresis pseudo inverse compensator and dynamic surface control method, the asymmetric and saturated hysteresis can be effectively mitigated without the requiring the true hysteresis inverse model.
    • The method is to obtain the input signal of hysteresis by finding the optimal value of the PI performance index.
    • The performance and error conversion functions are introduced in the design process of the controller to ensure the predetermined performance indicators.
    • Compared with the backstepping control scheme, the DSC algorithm can eliminate

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return